4.7 Article

When Deep Learning Meets Digital Image Correlation

Journal

OPTICS AND LASERS IN ENGINEERING
Volume 136, Issue -, Pages -

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.optlaseng.2020.106308

Keywords

Convolutional Neural Network; Deep learning; GPU; Digital Image Correlation; Error Quantification; Photomechanics; Speckles

Categories

Funding

  1. French government research program Investissements d'Avenir through the IDEX-ISITE initiative [16-IDEX-0001 (CAP 20-25)]
  2. IMobS3 Laboratory of Excellence [ANR-10-LABX-16-01]

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This paper introduces the development of a CNN model (StrainNet) for retrieving displacement and strain fields from pairs of images of a flat speckled surface, and demonstrates its successful performance of measurements. Compared to DIC, StrainNet achieves competitive results in terms of metrological performance and computing time, making it a viable alternative for real-time applications.
Convolutional Neural Networks (CNNs) constitute a class of Deep Learning models which have been used in the recent past to resolve many problems in computer vision, in particular optical flow estimation. Measuring displacement and strain fields can be regarded as a particular case of this problem. However, it seems that CNNs have never been used so far to perform such measurements. This work is aimed at implementing a CNN able to retrieve displacement and strain fields from pairs of reference and deformed images of a flat speckled surface, as Digital Image Correlation (DIC) does. This paper explains how a CNN called 'StrainNet can be developed to reach this goal, and how specific ground truth datasets are elaborated to train this CNN. The main result is that StrainNet successfully performs such measurements, and that it achieves competing results in terms of metrological performance and computing time. The conclusion is that CNNs like StrainNet offer a viable alternative to DIC, especially for real-time applications.

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